ORCID

Abstract

From a psychological perspective, human behaviourreflectsunderlyingthoughtsand decision-making patterns, for example, consumer behaviour may correlate with the purchase decisions. In the fast-evolving e-commerce industry, predicting user behaviouris essential for enhancing marketing strategies, improving customer experiences, and increasing sales. However, traditional heuristic (e.g.market basket analysis) approaches to analysebuyer behaviour are often rigid and fail to adapt to complex consumer interactions. This research work develops a predictive model that analysesuser behaviour based on data such as historical purchasing patterns and demographic attributes. Based on a review of previous studies, Logistic Regression (LR) is utilized as theprimarymachine learning algorithm to estimate the likelihood of user performing specific actionsincluding churning and conversion rate. The dataset undergoes preprocessing steps, including data cleaning, feature selection, and normalization, toenhance model accuracy.Evaluationmetrics, including accuracy, confusion matrix, precision, recall and F1-Score are used to ensure the model’s performance is reliable and effective. Unlike traditional heuristic approaches, this data-driven method offers a scalable and adaptable solution for behaviourprediction. The findings of this research have the potential torevolutionize e-commerce by providing businesses with actionable insights into consumer behaviour. By leveraging predictive analytics, companies can implement targeted marketing campaigns, personalize recommendations, and improve customer retention strategies.Additionally, this study highlights the significance of behavioural modellingin detecting early signs of customer churn, allowing businesses to take proactive measures. Ultimately, this research contributes to the growing field of data-driven decision-making, offering a scalable and adaptable solution for understanding and predicting user behaviourin online shopping environments.

Publication Date

2025-10-16

Publication Title

Journal of Informatics and Web Engineering

Volume

4

Issue

3

Acceptance Date

2025-06-22

Deposit Date

2026-07-08

Funding

The authors received no funding from any party for the research and publication of this article

Keywords

logistic regression, Predictive Analytics, user behaviour, machine learning

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